Kernel machines and additive fuzzy systems: Classification and function approximation

Yixin Chen, James Z. Wang

Research output: Contribution to conferencePaperpeer-review

22 Scopus citations

Abstract

This paper investigates the connection between additive fuzzy systems and kernel machines. We prove that, under quite general conditions, these two seemingly quite distinct models are essentially equivalent. As a result, algorithms based upon Support Vector (SV) learning are proposed to build fuzzy systems for classification and function approximation. The performance of the proposed algorithm is illustrated using extensive experimental results.

Original languageEnglish (US)
Pages789-795
Number of pages7
StatePublished - Jul 11 2003
EventThe IEEE International conference on Fuzzy Systems - St. Louis, MO, United States
Duration: May 25 2003May 28 2003

Other

OtherThe IEEE International conference on Fuzzy Systems
CountryUnited States
CitySt. Louis, MO
Period5/25/035/28/03

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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